Exercise 1: Barplots and Distributions

1- Load the data from small_file.txt using read_delim Plot out a barplot of the lengths of each sample from category A Start by filtering the data to keep only Sample A samples small %>% filter(Category == “A”) Pass this filtered tibble to ggplot Your x aesthetic will be Sample and your y will be length Since the value in the data is the bar height you need to use geom_col   Plot out a barplot (using geom_bar) of the mean length for each category in small.file You will need to set stat=“summary”, fun=“mean” in geom_bar so it plots the mean value   Add a call to geom_jitter() to the last plot so you can also see the individual points Colour the points by Category and decrease the width of the jitter columns to get better separation. Make sure height is set to 0 If you don’t want to see the legend then you can set show.legend=FALSE in geom_jitter.   2- Load the data from expression.txt using read_delim. Plot out the distribution of Expression values in this data. You can try both geom_histogram and geom_density. Try changing the color and fill parameters to make the plot look prettier. In geom_histogram try changing the binwidth parameter to alter the resolution of the distribution.   3- Load the data from cancer_stats.csv using read_delim. Plot a barplot (geom_col) of the number of Male deaths for all Sites. (x=Site, y=Male Deaths) make sure you let the RStudio auto-complete help you to fill in the Male Deaths column name so you get the correct backtick quotes around it.   You won’t be able to show all of the categories so just show the first 5 (cancer %>% slice(1:5) %>% ggplot…)

 4- Load the data from child.variants.csv. Create a new variable in child.variants loaded from Child_Variants.csv called Good using mutate and if_else. The value should be “GOOD” if QUAL == 200 otherwise it should be “BAD” Plot out a violin plot, using geom_violin() of the MutantReads for the two Good categories.

data_small <- read.delim('C://Users//praba//Desktop//uca1//M1//R programming//class-5 workshop-2//small_file.txt',header=TRUE, stringsAsFactors=FALSE)
head(data_small, 5)

Plot out a barplot of the lengths of each sample from category A Start by filtering the data to keep only Sample A samples small %>% filter(Category == “A”) Pass this filtered tibble to ggplot Your x aesthetic will be Sample and your y will be length Since the value in the data is the bar height you need to use geom_col

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.2
library("dplyr")
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
data_small %>% filter(Category == "A") %>% ggplot(aes(x = Sample, y = Length)) + geom_col(col="blue2",fill="red")

data_small %>% ggplot(aes(x = Category, y = Length,)) + 
  geom_bar(aes(fill= Category),stat="summary" ,col="blue2", fun="mean",show.legend = FALSE)

data_small %>% ggplot(aes(x = Category, y = Length)) + geom_bar(stat="summary" ,col="blue2",fill="yellow", fun="mean",width=0.3)+ geom_jitter(aes(col =Category),height=0,width=.02,show.legend = TRUE)

2- Load the data from expression.txt using read_delim. Plot out the distribution of Expression values in this data. You can try both geom_histogram and geom_density. Try changing the color and fill parameters to make the plot look prettier. In geom_histogram try changing the binwidth parameter to alter the resolution of the distribution. ===

# C://Users//praba//Desktop//uca1//M1//R programming//class-5 workshop-2//

setwd('/Users/praba/Desktop/uca1/M1/R programming/class-5 workshop-2')
data_expression= read.delim('expression.txt',header=TRUE, stringsAsFactors=FALSE)
head(data_expression,6)
data_expression %>%
  ggplot(aes(x=Expression)) + 
  geom_histogram(fill="yellow", colour="red")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

data_expression %>%
  ggplot(aes(x=Expression)) + 
  geom_histogram(binwidth = .5, fill="yellow", colour="red")

data_expression %>%
  ggplot(aes(x=Expression)) + 
  geom_density(colour="red", alpha=0.1,fill="green")

3- Load the data from cancer_stats.csv using read_delim.

Plot a barplot (geom_col) of the number of Male deaths for all Sites. (x=Site, y=Male Deaths) make sure you let the RStudio auto-complete help you to fill in the Male Deaths column name so you get the correct backtick quotes around it.

You won’t be able to show all of the categories so just show the first 5 (cancer %>% slice(1:5) %>% ggplot…)

setwd('/Users/praba/Desktop/uca1/M1/R programming/class-5 workshop-2')
cancer_data<- read.csv("cancer_stats.csv")
head(cancer_data)
cancer_data%>% slice(1:5) %>% ggplot(aes(x = Site, y = Male.Deaths)) + geom_col( ,col="blue2",fill="yellow",width=1)

cancer_data%>% slice(1:5) %>% ggplot(aes(x = Site)) + geom_bar(col="blue2",fill="yellow",width=1)

cancer_data%>% slice(1:5) %>% ggplot(aes(x = Site, y = Male.Deaths)) + geom_bar(stat="identity" ,col="blue2",fill="yellow",width=1)

4- Load the data from child.variants.csv.

Create a new variable in child.variants loaded from Child_Variants.csv called Good using mutate and if_else. The value should be “GOOD” if QUAL == 200 otherwise it should be “BAD” Plot out a violin plot, using geom_violin() of the MutantReads for the two Good categories. ===

child_data<- read.csv("Child_Variants.csv")
head(child_data)
child_data %>% mutate(child_data, Good = if_else(QUAL == 200,"GOOD", "BAD" ) 
)
child_data
child_data %>% mutate(child_data, Good = if_else(QUAL == 200,"GOOD", "BAD" ))  %>% ggplot(aes(y = log2(MutantReads), x = Good),) + geom_violin(col="blue2",fill="yellow")

Exercise 2: Annotation, Scaling and Colours

Use theme_set to set your ggplot theme to be theme_bw with a base_size of 12. Replot one of your earlier plots to see how its appearance changed.

In the cancer barplot you did in exercise 1 you had to exclude sites because you couldn’t show them on the x axis. Use the coord_flip transformation to switch the x and y axes so you can remove the slice function which restricted you to 5 sites, and show all of the sites again.   Load the data from brain_bodyweight.txt   Plot a scatterplot of the brain against the body   Change the axis labels (xlab and ylab) to say Brainweight (g) and Bodyweight (kg) and add a suitable title (ggtitle).   Both brainweight and bodyweight are better displayed on a log scale – try implementing this in one of the ways below   Turn the axes into log scale axes (scale_x_log10 and scale_y_log10) Modify the data to be log transformed when creating the aesthetic mapping (pass the column name into log10() when defining the aesthetic mapping in aes()) Use mutate to modify the original data before passing it to ggplot   Color the plot by Category, and change the colours to use the ColorBrewer “Set1” palette (scale_colour_brewer)   Change the ordering of the categories to be “Domesticated”, “Wild”, “Extinct”

Create a barplot of the brainweight of all species, coloured by their bodyweight. Use a custom colour scheme for the colouring of the bars. You will again need to use a log scale for the brain and bodyweight.

Use theme_set to set your ggplot theme to be theme_bw with a base_size of 12. Replot one of your earlier plots to see how its appearance changed.

theme_set(theme_bw(base_size=12))
data_small %>% filter(Category == "A") %>% ggplot(aes(x = Sample, y = Length)) + geom_col(col="blue2",fill="red")

theme_set(theme_bw(base_size=12))
data_small %>% ggplot(aes(x = Category, y = Length,)) + 
  geom_bar(aes(fill= Category),stat="summary" ,col="blue2", fun="mean",show.legend = FALSE)

theme_set(theme_bw(base_size=12))
data_small %>% ggplot(aes(x = Category, y = Length)) + geom_bar(stat="summary" ,col="blue2",fill="yellow", fun="mean",width=0.3)+ geom_jitter(aes(col =Category),height=0,width=.02,show.legend = TRUE)

theme_set(theme_bw(base_size=12))

data_expression %>%
  ggplot(aes(x=Expression)) + 
  geom_density(colour="red", alpha=0.1,fill="green")

theme_set(theme_bw(base_size=12))
child_data %>% mutate(child_data, Good = if_else(QUAL == 200,"GOOD", "BAD" ))  %>% ggplot(aes(y = log2(MutantReads), x = Good),) + geom_violin(col="blue2",fill="yellow")

theme_set(theme_bw(base_size=12))

cancer_data%>% slice(1:5) %>% ggplot(aes(x = Site, y = Male.Deaths)) + geom_col( ,col="blue2",fill="yellow",width=1)

##In the cancer barplot you did in exercise 1 you had to exclude sites because you couldn’t show them on the x axis. Use the coord_flip transformation to switch the x and y axes so you can remove the slice function which restricted you to 5 sites, and show all of the sites again.

theme_set(theme_bw(base_size=12))

cancer_data%>% ggplot(aes(x = Site, y = Male.Deaths)) + geom_col( ,col="blue2",fill="yellow",width=1)+coord_flip()
## Warning: Removed 5 rows containing missing values (`position_stack()`).

##Load the data from brain_bodyweight.txt

Plot a scatterplot of the brain against the body

brain_data<- read.delim("brain_bodyweight.txt")
head(brain_data,6)

###Plot a scatterplot of the brain against the body

brain_data %>% ggplot(aes(brain,body)) + geom_point(col="red")

###Change the axis labels (xlab and ylab) to say Brainweight (g) and Bodyweight (kg) and add a suitable title (ggtitle).

brain_data %>% ggplot(aes(brain,body)) + geom_point(col="red")+
ggtitle("gg title")+
xlab("Brainweight(g)")+
ylab("Bodyweight(kg)")

###Both brainweight and bodyweight are better displayed on a log scale – try implementing this in one of the ways below Turn the axes into log scale axes (scale_x_log10 and scale_y_log10) Modify the data to be log transformed when creating the aesthetic mapping (pass the column name into log10() when defining the aesthetic mapping in aes()) Use mutate to modify the original data before passing it to ggplot

brain_data %>% ggplot(aes(log10(brain),log10(body))) + geom_point(col="red")+
ggtitle("gg title_log")+
xlab("Brainweight(g)")+
ylab("Bodyweight(kg)")

brain_data %>% ggplot(aes(x=brain, y=body)) + geom_point(aes(colour=Category))+
ggtitle("gg title")+
xlab("Brainweight(g)")+
ylab("Bodyweight(kg)")+scale_x_log10() + scale_y_log10()

###Color the plot by Category, and change the colours to use the ColorBrewer “Set1” palette (scale_colour_brewer)

brain_data %>% ggplot(aes(x=brain, y=body)) + geom_point(aes(colour=Category))+
ggtitle("gg title")+
xlab("Brainweight(g)")+
ylab("Bodyweight(kg)")+
scale_x_log10() +
scale_y_log10() +
scale_color_brewer(palette = "Set1")

###Change the ordering of the categories to be “Domesticated”, “Wild”, “Extinct”

brain_data %>% mutate(Category= factor(Category,levels=c("Domesticated","Wild","Extinct"))) %>%
ggplot(aes(x=brain, y=body)) + geom_point(aes(colour=Category))+
ggtitle("gg title")+
xlab("Brainweight(g)")+
ylab("Bodyweight(kg)")+
scale_x_log10() +
scale_y_log10() +
scale_color_brewer(palette = "Set1")

###Create a barplot of the brainweight of all species, coloured by their bodyweight. Use a custom colour scheme for the colouring of the bars. You will again need to use a log scale for the brain and bodyweight.

brain_data%>% slice(1:5) %>%  ggplot(aes(x=Species,y=brain,fill= body)) + geom_bar(stat="identity" ,col="blue2",width=1)+
  scale_fill_gradientn(colours=c("red","orange","green","blue","skyblue"))+
labs(title = "Brain Weight of Species Colored by Log Body Weight") +coord_flip()

brain_data %>%  ggplot(aes(x=Species,y=log10(brain),fill= log10(body))) + geom_bar(stat="identity" ,col="blue2",width=1)+
  scale_fill_gradientn(colours=c("red","orange","green","blue","skyblue"))+
labs(title = "Brain Weight of Species Colored by Log Body Weight") +coord_flip()

brain_data %>% slice(1:5) %>% ggplot(aes(x=Species,y=log10(brain),fill= log10(body))) + geom_bar(stat="summary" ,col="blue2",width=1)+
  scale_fill_gradientn(colours=c("red","orange","green","blue","skyblue"))
## No summary function supplied, defaulting to `mean_se()`

Exercise 3: Summary Overlays

Load the data in treatments.csv.   Plot a stripchart of the four conditions using geom_jitter()   Overlay a boxplot of the same data along with the raw points   Adjust the size and width of spread of the points in geom_jitter to something sensible Adjust the size of the lines in the boxplot Make sure geom_boxplot is drawn first so you can see everything Try colouring the points by the condition to see if it’s any clearer.   Plot the same data as a barplot with errorbars for the Standard Error of the Mean (SEM) Use a geom_bar for the barplot with stat=”summary” and then use stat_summary with a geometry of errorbar with the default mean_se values.

Take the same treatment data and pre-calculate a mean and sd from it using group_by and summarise. Use these pre-calculated values to plot out the same barplot as before. Replot the stripchart, but instead of overlaying a boxplot, use stat_summary to just add a bar to indicate the mean. === ###Load the data in treatments.csv. ===

treatment_data= read.csv("treatments.csv")
head(treatment_data)

###Plot a stripchart of the four conditions using geom_jitter()

treatment_data %>% ggplot(aes(x=Sample,y=Measure))+geom_jitter(col="red")

###Overlay a boxplot of the same data along with the raw points ===

treatment_data %>% ggplot(aes(x=Sample,y=Measure))+geom_boxplot(size=.5)

treatment_data %>% ggplot(aes(x=Sample,y=Measure,color=Sample))+geom_boxplot(size=.5,col="gray")+geom_jitter(width=1,size=3)

treatment_data%>%  ggplot(aes(x=Sample,y=Measure,fill= Sample)) + geom_bar(stat="summary" ,col="blue2",width=1)
## No summary function supplied, defaulting to `mean_se()`

###Plot the same data as a barplot with errorbars for the Standard Error of the Mean (SEM) Use a geom_bar for the barplot with stat=”summary” and then use stat_summary with a geometry of errorbar with the default mean_se values. ===

treatment_data%>%  ggplot(aes(x=Sample,y=Measure,fill= Sample)) + geom_bar(stat="summary" ,col="blue2",width=1)+stat_summary(geom="errorbar")
## No summary function supplied, defaulting to `mean_se()`
## No summary function supplied, defaulting to `mean_se()`

###Take the same treatment data and pre-calculate a mean and sd from it using group_by and summarise. Use these pre-calculated values to plot out the same barplot as before.

treatment_data %>% group_by(Sample)%>%summarize(avg_Measure = mean(Measure),sd_Measure = sd(Measure))
treatment_data %>% group_by(Sample)%>%summarize(avg_Measure = mean(Measure),sd_Measure = sd(Measure))%>%  ggplot(aes(x=Sample,y=sd_Measure)) + geom_bar(stat="summary" ,col="blue2",fill="yellow",width=1)+stat_summary(fun = mean)+stat_summary(geom="errorbar")
## No summary function supplied, defaulting to `mean_se()`
## No summary function supplied, defaulting to `mean_se()`
## Warning: Removed 4 rows containing missing values (`geom_segment()`).

treatment_data %>% group_by(Sample)%>%summarize(avg_Measure = mean(Measure),sd_Measure = sd(Measure))%>%  ggplot(aes(x=Sample,y=avg_Measure,ymin=avg_Measure-sd_Measure,ymax=avg_Measure+sd_Measure,fill= Sample)) + geom_bar(stat="identity" ,col="blue2",width=1)+geom_errorbar()

Exercise 4: Faceted smoothing

Load iris dataset and reproduce this plot.

data(iris)

iris
iris %>% ggplot(aes(x = Sepal.Length, y = Sepal.Width))+ geom_point(aes(col =Species),size = 1)+geom_density2d(aes(col =Species))+theme_light()+ggtitle("IRIS")

# iris %>% ggplot(aes(x = Petal.Length, y = Petal.Width))+ geom_point(aes(col ="All"),size = 1)+ geom_smooth(se = TRUE) 
# install.packages("patchwork")
library(patchwork)
## Warning: package 'patchwork' was built under R version 4.3.2
# install.packages("gridExtra")
library(gridExtra)
## Warning: package 'gridExtra' was built under R version 4.3.2
## 
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
## 
##     combine
plot_1 <- ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = "All")) +
  geom_point() +
  geom_smooth(se = TRUE) +
  labs(
       x = "Petal Length",
       y = "Petal Width")


plot_2 <- ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Species)) +
  geom_point() +
  geom_smooth(se = TRUE) +
  facet_wrap(. ~ Species, scales = "free") +
  xlab("Petal Length")+
  ylab("Petal Width")
  # labs(
  #      x = "Petal Length",
  #      y = "Petal Width")


grid.arrange(plot_1, plot_2,nrow=2)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

# plot_1+ plot_2
iris %>% mutate(Species="All") %>% bind_rows(iris)%>%ggplot( aes(x = Petal.Length, y = Petal.Width, color = Species)) +
  geom_point() +
  geom_smooth(se = TRUE) +
  facet_wrap(. ~ Species, scales = "free")+labs(
       x = "Petal Length",
       y = "Petal Width")
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Exercise 5: Bubble-plot

Load mtcars dataset and reproduce this plot.

mtcars 
mtcars %>% ggplot(aes(x = mpg, y = qsec, size = disp, color = factor(am))) +
  geom_point(alpha = 0.7,) +
  scale_size_continuous() +  # Adjust the range of bubble sizes
  labs(
    title = "Bubble Plot with ggplot2 for ",
    x = "miles per gallon",
    y = "1/4th mile time",
    size = "Displacement",
    color = "gear",
  )

mtcars %>%
  ggplot(aes(x = mpg, y = qsec, size = disp, color = factor(am))) +
  geom_point(alpha = 0.7) + 
  scale_size_continuous(name = "Displacements") +
  scale_color_discrete(name = "Gear", breaks = c(0, 1), labels = c("Manual", "Automatic")) +
  labs(
    title = "Bubble Plot with ggplot2",
    x = "Miles per Gallon",
    y = "1/4th Mile Time"
  )

Exercise 6: Economist style

Load economics dataset and reproduce this plot. (Hint: you will need the ggthemes package)

economics 
# install.packages("ggthemes")
economics %>% ggplot(aes(x=date,y=uempmed))+geom_line()+theme_linedraw()+labs(
    title = "Median duration of unemployment[weeks]",
    y = "Median duration of unemployment[weeks]",
    
  )+
theme(plot.background = element_rect(fill = "grey92"))+
theme(plot.title = element_text(face = "bold"))+
theme(axis.title.y = element_text(face="bold"))+
theme(axis.title.x = element_blank())+
  theme( panel.grid.major.x = element_blank())+
  theme(panel.grid.minor.x = element_blank())+
  theme(panel.grid.minor.y = element_blank())+
  theme(panel.grid.major.y = element_line(color="gray80",size = .8 ) )+
  ylim(0, 25)
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Exercise 7: Msleep dataset

Load msleep dataset.

  1. Plot out a scatterplot of brainwt vs bodywt.

  2. Plot out the distribution of awake for all categories of vore on the same plot. Change the transparency of colors.

  3. Plot out a boxplot of sleep_total for all categories of vore, colored by vore.

  4. Plot out a barplot of the number of mammals for all categories of conservation.

  5. Plot out a scatterplot of sleep_total vs bodywt in log scale. Use a facet to separate the data for the categories of vore (except NA).

=== #### Load msleep dataset.

msleep

1. Plot out a scatterplot of brainwt vs bodywt.

msleep %>% ggplot(aes(x=brainwt,y=bodywt)) + geom_point(col="red")+
ggtitle("scatterplot of brainwt vs bodywt")+
xlab("Brainweight")+
ylab("Bodyweight")
## Warning: Removed 27 rows containing missing values (`geom_point()`).

msleep %>% ggplot(aes(x=log10(brainwt),y=log10(bodywt))) + geom_point(col="red")+
ggtitle("scatterplot of brainwt vs bodywt in log10 scale")+
xlab("Brainweight")+
ylab("Bodyweight")
## Warning: Removed 27 rows containing missing values (`geom_point()`).

### 2. Plot out the distribution of awake for all categories of vore on the same plot. Change the transparency of colors.

msleep%>% ggplot(aes(x=awake, fill=vore)) +
  geom_density(aes(col=vore), alpha=0.3)+
  labs(
    title="distribution of awake for all categories of vore"
  )+
  theme_minimal()+
  theme(legend.position="left")

3. Plot out a boxplot of sleep_total for all categories of vore, colored by vore.

msleep %>% ggplot(aes(x=vore,y=sleep_total))+geom_boxplot(aes(col=vore))+
  ggtitle("boxplot of sleep_total for all categories of vore")

4. Plot out a barplot of the number of mammals for all categories of conservation.

conservation_counts_1 <- msleep %>%group_by(conservation) %>% summarise(count = n())
conservation_counts_1
ggplot(conservation_counts_1, aes(x = conservation, y = count)) +
  geom_bar(aes(fill= conservation),stat = "identity", color = "black") +
  labs(
    title = "Number of Mammals for Each Conservation Category",
    x = "Conservation Category",
    y = "Number of Mammals"
  )

5. Plot out a scatterplot of sleep_total vs bodywt in log scale. Use a facet to separate the data for the categories of vore (except NA).

msleep_filtered <- msleep %>%
  filter(!is.na(vore))

ggplot(msleep_filtered, aes(x = log10(bodywt), y = log10(sleep_total),col=vore)) +
  geom_point() +
  labs(
    title = "Scatterplot of log(Sleep Total) vs log(Body Weight) by Vore",
    x = "log(Body Weight)",
    y = "log(Sleep Total)"
  )

msleep_filtered <- msleep %>%
  filter(!is.na(vore))

ggplot(msleep_filtered, aes(x = log10(bodywt), y = log10(sleep_total),color = vore)) +
  geom_point() +
  facet_wrap(~ vore, scales = "free") +
  labs(
    title = "Scatterplot of log(Sleep Total) vs log(Body Weight) by Vore",
    x = "log(Body Weight)",
    y = "log(Sleep Total)"
  )